We live in a time of plentiful choices. Making the most of it, many organisations or start-ups in globally are creating their impact, in the shortest possible time, by means of integrating multiple channels of customer service in their businesses. These channels include phone support, email and chat support, social media support, or even text support.
Nowadays, integration of multi-channel customer services is one of the most important factors for a business to consider, due to many reasons. By providing a range of options to the customer, businesses in globally are attracting and growing their loyal customer base significantly. They build high value by affecting the way in which people are making purchase decisions nowadays.
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In addition, offering abundant choices to your customers is a forerunner for a business to maintain its presence, meet the customer needs round the clock irrespective of their location, and empower customers to take the right decision. To assist you further, let’s have a detailed look at the concept of multi-channel customer service.
Business Efficiency through Multi Channel Integration
Multi channel customer service holds huge potential in delivering business efficiency and excellent customer experiences. The customer experience offered by integrating a multi channel strategy creates a more personalised, optimised and responsive outlook of a business in competitive and transforming industries.
The primary channels that attract the maximum investment of various small or big organisations are 83% online or self-service – digital, 62% mobile apps, 34% email and surprisingly, 57% of investment is focused on the phone or voice channel while 18% towards face to face channels.
This clearly emphasises on the potential that opens up for a business with the integration of multi channel customer service. Along with that, the level of exposure of the customer base to your business improves, as you cater to the needs of your customers by all means, and without falling short of their expectations.
Are you increasing the investment in multi channel customer service this year, to attain higher business value in your particular industry?
If you too, plan to join the forces of digital transformation and differentiate your business from the existing competitors, then you must make the most of these consumer channel preferences, and make only the right decisions, most importantly in the right direction, to optimise your customer service experience.
The top most channels utilised by Australian consumers, to avail customer service, are through phone conversation – 62%, via self-service website – 41% and in person – 45%.
The Prospective Channels of Customer Service and their Importance
Organisations that believe in delivering exceptional customer support, reach out to their consumers through all of those channels where their customers are present. Thus:
If your existing customer base includes avid user of emails, opt for an instant email support.
If they like to reach your business on Twitter, make this platform as your potential channel of customer support to access your customers efficiently.
In continuation of the above stated Fifth Quadrant research, keeping a strong grip on all the available channels of customer support will be the only key factor in differentiating your business and moving it along to a true market leader.
Email: This is undoubtedly the most non negotiable channel for all types of businesses. Almost 91% of consumers utilise an email service, everyday. This is the easiest means of building instant rapport with your customer base.
Social Media: Social networks are now the most excellent means of accessing your customer and to grow your business. Companies who use social networks as customer support channels have 15% lower churn rates than the ones who don’t.
Self-service Knowledge Base: As the name depicts, the self-service knowledge base is extremely useful to help your customers get to know you better, without being present for their assistance, through a live channel of customer support. You can deliver exceptional assistance 24/7 with just a small team.
Voice or live Chat:Phone support is old-fashioned but considered as the fastest means of communication between your business and the customer base. In fact, phone assistance accounts for almost 68% of the speediest interactions. Similarly, 44% of customers say that having a live chat support during an online purchase creates a trustworthy relationship with the service providers and is accounted for, as the top feature a website could offer.
Multi Channel Customer Service – A mean of seamless consumer experience
Your customer wants to be able to contact you with whatever device they hold in their hands and that is what your business needs to do – make itself accessible, by all possible means, for your customer’s satisfaction.
Integrate many digital platforms and provide seamless consumer experience through different channels. If you manage to reach out to your consumer base through numerous channels, your business will be providing fantastic experience to its customers, which is hard to give up.
Gain more loyalty and trust with your instant customer service. Stay responsive to your customers and effectively provide them with updated information and flawless support.
In today’s fast paced and transforming world, a business that shows itself invested in providing exceptional support to its customers, by making the most of the multi channel customer service, will go a long way in building long lasting relationships with its customers and successfully make its mark.
There’s an industrial revolution under way in businesses across the world, and it is all about automation. Businesses are embracing machine learning and artificial intelligence to make better decisions automatically. And the reason for this revolution is the comparative strengths of humans and computers.
Computers are strongest at repetitive tasks, mathematics, data manipulation and parallel processing. So long as a task can be defined as a procedure, a computer can do that task over and over again, without getting tired, giving the same results each time. Computers can manipulate numbers and data in volume much faster than any human.
Several years ago I went back to university to do a masters degree, and after a 25 year break from university I was out of practice at mathematics. Imagine my excitement and relief when I discovered that now there is software that will do algebra and calculus for me! And computers can do more than one thing at a time. Have you ever tried to rub your belly and tap your head at the same time? I can’t do both actions simultaneously. But modern computer networks are powerful, able to routinely do dozens of different processes at once.
This does not mean that humans are obsolete. What humans are much more skilled than machines at are communication and engagement, context and general knowledge, creativity and empathy. When I have a frustrating problem, I want to talk to a human. Someone who will understand my exasperation, listen to my experience and make me feel valued as a customer, whilst also solving my problem for me. Humans are much better at common sense than computers, instantly recognizing when a decision doesn’t make sense. And humans can be creative. I recently heard music composed by a computer, and I’m sure that song won’t make it into the Top 40!
Customer Service
Recently I had a conversation with the manager of a call centre that dealt with hundreds of customer service issues each day. In order to ensure the quality of the service and advice, the call centre operators were given scripts and were commanded to follow those scripts without changing a word. The problem was that both staff and customers became frustrated. Staff felt bored and unchallenged, and customers with non-standard problems felt like they weren’t being heard. Staff turnover increased, and customer satisfaction levels dropped.
Customer Satisfaction
The manager then tested using chatbots to answer simpler questions from customers, freeing up the human operators to deal with non-standard enquiries. This was a situation where computers had a comparative advantage over humans. The call center processes were fully defined, operating at scale, and the scripted answers were correct. The results spoke for themselves. Computers were much better at helping with the repetitive enquiries, and humans were better at dealing with the unusual enquiries. Staff engagement increased, as did customer satisfaction.
This has implications for human resources and process innovation. Processes that require humans to do repetitive, well defined tasks can be replaced by artificial intelligence. This frees up staff to do what humans are best at:
asking the right questions,
applying common sense,
creating new solutions,
evangelising new ideas, and
generating sales and profit.
Let your humans be human. Free them from repetitive tasks. Change job descriptions to focus on human strengths, and hire people who best embody the comparative advantages of humans. Look for human processes that are well defined and repetitive, and enhance the process by introducing artificial intelligence. Some ways company have started to incorporate artificial intelligence and machine learning into their processes include:
There are even some companies out there that have started automating the automation, like DataRobot. Instead of hiring and training up a data scientists, the arcane process of building predictive models, once the sole domain of data scientists, can all be automated. The system automatically builds predictive models based on your data, freeing up your humans to be human, to be better conversational AI specialists.
Based in Singapore, Colin is the Director, Customer Success and Lead Data Scientist, APAC for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Over his career, Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. He frequently speaks at various global conferences. Colin is a firm believer in data-based decision making and applying AI. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
The other day, my 73-year old father, was grumbling about something he read in the news about automation of processes at the local bank.
“People don’t talk anymore. In my day, customer service meant talking to someone, saying hello, asking how your day was. Now it’s a recorded voice or reading tiny print online. Customer service is dead.”
That got me thinking. The role of a traditional customer service representative has evolved over the years. Once the domain of primarily the service and hospitality staff, the role of customers and our relationships with them has seen several costume changes — the phone IVR, surveys, forms, smiling uniformed people, you name it. But even as the modes change, the role of customer services and engagement has only just increased. Today customer relationships have become a full-fledged industry. 70% of buying experiences are based on how the customer feels they are being treated. The more people feel they’re being listened to, the happier they are and the more money they’ll spend — or that’s the hope. An Adobe report even suggests that customer service can deliver a higher ROI than marketing. Customer service, once upon a time, used to be about happy people, lots of solicitous questions and a solution with a smile. While it’s certainly true that the human factor seems to have declined over the years, the key tenets of a human interaction customer service have remained — conversations, solutions and a smiling demeanor.
So can a bot — the latest entrant into the customer service role — actually deliver these admittedly-human qualities?
Chatbot as the perfect concierge
Businesses that recognize how much time consumers spend on messaging apps such as Facebook Messenger and Slack have developed automated messaging technology. In 2015 messaging apps surpassed social networking apps, and chat apps have higher retention and usage rates than most mobile apps. Today, brands are looking at bots to become the next concierge, to understand what the customer wants, which direction they’re headed on, to involve them in interesting content, spread brand awareness and indeed, carry on conversations with a smile. But is all of that realistically possible?
On paper, it’s the perfect solution. Bots are machines, easily duplicated and incapable of human drama. They can be taught to function perfectly with a specific set of rules or through machine learning. These capabilities, limited as they are, can be trained to emulate the perfect customer service person’s skills — kindness, patience and solution-oriented. A machine can be taught to never be sarcastic and to always have a listening ear. And because it doesn’t have human failings such as fatigue or just being an asshole, it’s becoming an increasingly widespread phenomenon.
Any company with a chatbot interacting in the marketplace has the opportunity to gain valuable customer information. This has benefits in several areas — more personalization, targeted marketing, sales strategies as well as manpower allocation.
Not there yet
While bots were also a hot topic at the recent Corporate Social Media Summit, the jury were admittedly slightly skeptical. Bots are still very much in their nascent stage. And there have been several failures. In the rush to develop the next Siri or Cortana for their businesses, what most companies have ended up with are simplistic, underdeveloped tech with limited capabilities and faulty data. Of course there are the filthy people of the internet. It took less than 24 hours for Microsoft’s Tay to turn into into a filthy Nazi racist troll and two weeks for the cute little hitchbot to become roadside shrapnel. Even if the world were a perfect place, everyone was sunshine and unicorns and keeping empathy and other qualities aside, the actual functions and solutions given to customers by these bots need to work. That requires very skilled developers — but even they aren’t free of error.
Having said that, the possibilities for a bot are immense. Even though the big tech companies haven’t quite cracked how to make it work. Our co-founder Chris has a strong vision on the problems with Conversational AI, and perhaps more importantly — he offers solutions. He will share his vision 26th August on Startup Friday (still some spots left) and will start to share his vision in our blog series about Conversational AI that’s coming up here on Medium.
I know I will be paying attention. My own life have tons of bots — from the local Asian store from online magazines to Facebook to even my fitness wearable. Chatbots might very well be the face of the future one day. Now if they only knew how to smile.
I remember reading an article almost ten years ago talking about how teens were sending over 40 texts a day on average. The tone of the article was incredulous, but the statistic pales in comparison to how we exist online now. Speaking personally, it’s not implausible I send off 40 messages before 10 AM in my morning inbox check in. Sarah Guo, a partner of Greylock, expressed it succinctly when she took to Medium: “More than a decade ago, academics such as Thurlow described a “communication imperative”—human beings are driven to maximize their communication volume and satisfaction. More recently, researchers have described it as compulsion.”
While constant connectedness is old news, technology has finally achieved a point it can leverage this behavior. As with all big shifts, there will be survivors and those who don’t adapt fast enough. Companies will need to change to a conversational mode of thought to maintain the experiences users expect and deliver the individuality anticipated.
People Always Talk
Nearly 25 years ago, Harvard Business Review wrote “today if you’re not on the phone or talking with colleagues and customers, chances are you’ll hear, “Start talking and get to work!” In the new economy, conversations are the most important form of work.” Conversations are how we track knowledge flows. Conversation flows are how people create value, share information, and illustrate how companies operate.
A cited example is McKinsey. McKinsey prides itself heavily on the intelligence of its members, and by an extension the true value of McKinsey over other firms is its extensive knowledge base. That knowledge is curated and developed through internal conversation and shared through internal conversation. In short, McKinsey is conversation.
We are entering a new age for product development – one dictated by the conversational economy. Broadly, the conversational economy is the catchall phrase for companies, products, and ideas built on, alongside, or relying heavily on a conversational interface. More simply, they are services that leverage conversation.
This definition is board, and intentionally so. While some apps like iMessage, Snapchat and email obviously fit into this definition, conversation works as a backbone in services like Facebook, customer service complaints, and online advertising as well. Finding a common backbone helps derive a working model for these services.
Between the myriad of mobile apps used every day, access to the internet, and the seemingly innate human need to feel connected, conversation based platforms are dominating our lives. We have effectively destroyed the asynchronous quality of day to day life. We persist online, and, consequently, our conversations with one another never really begin or end. This data stream is a jackpot for product creation.
Smarter Everyday
Artificial intelligence, in the eyes of the public, has snuck past an important threshold. Presentations by titans like Facebook and Google have assured that we are moving away from the robotic idea of natural language processing in a rigid sense to natural language understanding. In other words, instead of responding to a keyword or a phrase, computers are beginning to be able to understand sentence, paragraphs, and intent.
There are a variety of causes for this – improvement of machine learning and deep learning, Moore’s Law, and rate of mobile and app data collection, to name a few. Algorithms and software are taking on their own intelligence. Just the idea that failed outcomes can make systems better is an astounding twist compared to five years prior.
Additionally, we’re in the middle of the boom of ambient computing, the idea that our environments and surroundings are responsive. We don’t have to open our phone or flip open a laptop to be connected. On the way to work I may pass a few smart cameras, a plethora of listening iPhones and Galaxy phones, an Alexa, Chromecasts, and more. Despite this, I would characterize myself as one of the less connected people in my demographic. At every step of my day my voice can be heard, position tracked, and activity monitored. Being connected no longer has much to do with if our phone is on our person or if we’re behind a keyboard.
Although passive collection has subtly pushed past our natural aversion to share information with technologies we don’t understand and people we don’t know, this one-sided trade has come with the expectation of usability. When software doesn’t work or apps crash, we no longer blame ourselves, we blame companies. We are inundated with choices, but that means that we have little tolerance of poor experiences. Users are more empowered than ever in that they don’t have to subject themselves to experiences they don’t want or content they’d rather avoid. We so demand these freedoms that events like net neutrality rapidly cause public outcry and social faux pas by companies like EA tank sales.
Computing, connectedness, and data almost completely undermine how product managers need to think about designing products. The need to leverage conversation to deliver value has emerged as one of the most critical company problems. IDEO acquiring a data analytics company, giants like Apple acqui-hiring boutique companies with human-centric software, and Salesforce pushing Einstein all serve as mine canaries that even the most established companies are racing and struggling to adapt.
Buying In and Cashing Out
As George Box famously cited – all models are wrong, but some are useful. Where is the utility of viewing products as ongoing conversations?
A helpful place to start is in how companies have historically fended off competition. These ‘moats’ include things like brand loyalty, unique data sources, and intellectual property. However, as technologies like AI are more readily available via open source projects, cloud hosting and computing are only a few clicks away, and systems of engagement continually emerge, the traditional ideas of tech defensibility are evaporating. In a Greylock article on Medium, they wrote “In all of these markets, the battle is moving from the old moats, the sources of the data, to the new moats, what you do with the data.”
In another words, companies are now finding defensibility through the experiences they create. To create these experiences for customers in the conversational era, companies will have to harness existing behavior, respond personally, and work faster.
Harnessing existing behavior is an exercise in invisibility. The real frontier for conversational companies to generate solutions for problems before the consumer is even aware. For example, Facebook realized that people asked for recommendations on their newsfeeds. Instead of creating a new service, they had posts automatically update with reviews and locations. They created a new card that changed automatically depending on what a user wrote. As expressed by a product designer at Facebook: “We didn’t try to invent a completely new behavior; rather, we found an existing one and made it way better.”
To cite an example within my own career, food industry companies often lose hours if not days within food recall investigations. Tracking a faulty shipment through several distributors can be tricky. We worked to create a product that reads the complaint before the owner may even be aware it exists and start and investigation. By the time an owner is even aware there is a problem, a report is ready. By approaching complaints, invoices, and shipments and messages between companies, value can be created seamlessly in a second layer.
As I’ve written about before, personalization is an increasingly critical element of producing customer lifetime value. Harvard Business Review started to notice this trend in their research on customer service: “Even as artificial intelligence becomes embedded in everyday interactions; human conversation remains the primary way people make complex purchases or emotional decisions.” The fatal error in a lot of software products is focusing on company efficiency over consumer experience. While these changes may boost bottom line in the short term, they encourage competitor entry and consumer drop off.
Conversational AI apps have an obvious outlet for personalization, and the power behind them allow easy switching between automation and human elements. More simply: “these intelligent agents will facilitate one-on-one conversations between consumers and sales or customer service representatives rather than simply replacing human interaction.” Imagine a case where someone sits on a delayed flight and sends out an angry tweet. A conversational built system could find the message, tag it, and route it to an agent. While the agent delivers a personal response with an update, the system has already sent an alleviating reward of extra miles to the customer. The captain may be alerted of sentiment on the plane and deliver an announcement. While an autoresponder may have been cheaper, the customer will now remember the exceptional level of immediate service and is more likely to return. As information and computing become free, the real commodity becomes the personality of the person on the other end of the line.
In the shorter term, there’s a simpler way to think about AI adoption – people don’t trust what they don’t understand. In the classic product management advice, it’s best to start with a problem and move to solution. Leveraging conversation is a means to building a better product, but that doesn’t change what the bottom lines should be. In other words, “Bots do not need to be human to be human centered.”
Outside of the shift in new product priorities, another major implication is how we use the technologies we use currently. In a blog post, Dan Rover (sp?) declared that bot won’t replace apps, but inboxes were the new home screen. Our email, text messages, and more were queues demanding our intention and driving our usage.
Companies leveraging platforms like WeChat have been able to effectively create micro services and apps for things like ordering that have integrated seamlessly with how we act now. Bot companies that are able to daisy-chain onto conversations to do scheduling and commuter planning have shone in venture capital funding. It’s not inconceivable the next unicorn will have nothing to do with creating a new platform but layering effortlessly onto the ways we talk with those platforms now.
Speak Now
We talk online all the time, but computing has finally let us create value from that. Companies need to invest in ways to leverage these conversations to deliver seamless and personal content. This means focusing on personnel and focusing on alleviating frictions than automation. Companies that don’t value the communication imperative and connectedness of customers will soon find themselves lagging in experience, and, later, sales.
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A prime example of this is Amazon Web Services’ fast climb to dominance. Legacy systems like Oracle required costly deployments and developers, and setting up cloud instances on AWS is only a few clicks away. IaaS records have shown Amazon’s sheer dominance. Oracle, trying to defend by housing data and curating an elite brand, couldn’t compete with Amazon’s engagement accessibility.
Perhaps the most obvious implication of smart conversational apps is efficiency. However, despite all the news and hype around an artificial intelligence singularity, businesses – and their customers – still revolve around the interactions person to person. This means that products needs to be resolved around facilitating conversation, collecting information, and iterating form that information. The AI boom has made it easier than ever to facilitate personal conversations no matter where customers are online.
A must-read guide for enterprises with billions of conversations and millions of customers.
Modern call centre with millions of customers.
Enterprises are much more overwhelmed with conversations than ever before. Not only do they have to actively respond to customers over a myriad of channels like email, phone, social and livechat, they’re expected to give personal, relevant and fast responses. To tackle this problem, many organizations are looking at new technology to help them meet customer expectations. Some of the most notable are AI chatbots, self-service knowledge bases and good old Interactive Voice Response (IVR) systems. The problem? These all aim to lessen the time customers spend with agents.
While people do like self service for speed and convenience, majority still want to be able to talk to a person in times of need, or at important turning points in their life. Curiously, while we’re moving more towards a more digital and self-service world, most consumers still want the ‘human touch’ in their service communications.
Human touch is more important than just automation.
The challenge is to provide highly personalized and relevant offerings to meet both customer and business goals, all the while delivering the experience through the customer’s natural mediums of interaction. Counterintuitively, the likeliest solution to bring the human element back into customer conversations is though technology and big data. So, what should you look for in a technology that will give you both customer satisfaction and maximize revenue?
Multichannel Conversations
At the basics, an organization’s communication channels should be in one view. That means a business should be able to see and reply to customers by email, phone, livechat, social media, forums and wherever they could be talking to you, or about you, on one platform. Why? Convenience and transparency.
Convenient Conversations
A single platform for the entire range of conversation channels is much more efficient for customer-facing agents. Often, they have to switch between multiple channels to check for new customer interactions, and unfortunately, miss some communications here and there. With one view for conversations, they save on time, and reduces the chance they will miss communications from less monitored channels.
The convenience isn’t just for agents. Customers want to interact with brands through their medium of choice. 51% of U.S. consumers are loyal to brands that interact with them through their preferred channels of communication. Younger consumers especially, want to interact with large organizations via instant messaging channels where they can use natural language. Having all channels on one platform allows agents to have visibility across all channels, instead of doing well on a few and lagging on others.
Transparent Conversations
In so many organizations, a different team handles a different channel. They are responsible for that channel, and that channel only. But the customer is dynamic. They might reach out on one channel, and upon finding that it isn’t fast enough or substantial enough to resolve their problems, they will switch channels.
The ‘different team, different channel’ approach doesn’t account for the customer’s flexibility, resulting in multiple replies or inconsistent replies from two different people, both creating bad customer experiences. With multiple channels on one view, conversations are transparent. Conversations from the same customer are stitched together, and the same person can handle issues without making the customer’s journey difficult.
Holistic Customer View
In an enterprise with multiple departments, systems and channels, it’s necessary to have a collective view of the customer. A single customer view (or a 360 degree view) is a complete profile of a customer, created from aggregated data points within an organization’s systems and channels. It collates data from multichannel communications and customer data platforms (like CRMs, analytics, marketing and legacy systems).
Customers often complain about the lack of continuity in their conversations and having to repeat themselves. Problems like this arise because agents have no visibility on what customers have said on a separate channel, or what customer information exists on a separate system. As such, interactions are treated as a completely new “ticket”, and in the worst cases, existing customers are seen as a new customer. With a single customer view, an agent can see a given customer’s conversational, transactional and behavioral data in one place. This not only improves time-to-answer by 20% – 80%, it also ensures customer information flow is consistent and continuous, reducing awkward moments like the ones above.
The use of a single customer view can even go beyond customer care activities. Integrated systems mean that there could be a seamless blend of sales, marketing and service activities through conversation. Having this feature marks the start of being able to use critical sources of data collectively. The key however, lies in how the customer intelligence is used. The following presents ways customer intelligence can be used to take control of conversations in providing exceptional customer experience and maximize revenue.
AI-assisted agents
Use of artificial intelligence (AI) in enterprises is not new. For decades they have been used to automate heavily manual processes to increase efficiency, accuracy and decrease costs. What is new, is the use of AI beyond processes to interactions. Use of AI opens up the potential to deliver personalized interactions and hyper-relevant offerings that are scalable.
Whether it’s the AI itself doing the talking, or an algorithm providing assistance to a human representative, online, or face-to-face, AI holds incredible potential to re-establish the human-to-human connection in an increasingly digital world. Check out some examples below.
Deliver relevant content and information with AI
Many organizations have invested heavily into user experience, self-service and knowledge management tools. Yet, it is still difficult and time-consuming for customers to find the right information when they need it. Companies like Zendesk have developed AI-powered virtual assistants that help customers self-serve. By processing natural language, the technology suggests articles in the knowledge base to help them resolve their problems on their own. Research has found that most people are open to using self-serve AI technology like this, and see it as faster and more convenient.
Other organizations like Woveon have built AI-powered response assistants to help agents have more productive conversations in real-time. As agents talk with customers, the response assistance helps guide conversations so better results can be achieved for both the customer and the business. It would suggest opportunities like ‘other customers like her also bought’, or ‘he mentioned credit cards, link to these articles from our blog to help him decide’.
Speed up resolution times
On average, a customer care specialist spends 20% of their time looking for information and context to resolve a customer’s problem. That’s one whole day in a work week! AI can help organize information so that it’s easily digestible and relevant to a customer’s enquiry. Woveon’s Intelligent Response framework for example, will change the information it displays to assist agents based on the flow of conversation. If a customer talks about their personal loan, their loan details pop up. If the conversation shifts to their lost credit card, their shipping details will surface and agents are prompted to cancel the lost card.
Instead of wasting time looking for information, AI assistance leave agents more time to build a relationship and take up on untapped customer opportunities. Customers also love a quick and productive interaction. 69% attributed their good customer service experience to quick resolution of their problem.
Reduce repetitive admin tasks to open doors for higher value interactions
Administrative tasks like After-call work (ACW) have been a constant headache for employees in customer-facing roles. Though they are necessary, it’s tedious, repetitive and and takes up too much time. Technology can help to reduce time spent on these menial tasks, leaving agents more time to build customer relationships and, in the process, make their jobs more productive and meaningful.
For example, Avaya has a natural language summarization tool to help agents process customer information post-call. Talkdesk automates call routing, where the customer is automatically paired with an agent with the best ability to solve their problem. Woveon can prioritize conversations real-time, based on customer importance, value, urgency, or a mixture of all factors.
Freeing up employee time away from menial tasks allow them to participate in higher-value activities.
Intelligent Analytics
There’s no doubt that data analytics is incredibly beneficial for customer conversations. The trick is knowing what data to use, how, and when.
Whatdata is being used matters because not all data is created equal. For example, rather than looking at metrics at a point in time (customer rated the agent 4 out of 5 for resolution), it’s much more important to look at the larger picture (that it took 3 calls and an hour on hold to get there).
Howdata is used is arguably more critical to conversational success. The key lies in knowing what datapoints to tie together, and what analysis to draw from it. A mesh of marketing and service data can show how a recent marketing campaign has affected conversation volume and NPS. A cluster analysis of related keywords in customer conversations can lead to discovery of a huge logistics flaw.
Whento use what data is of particular importance to customer-facing agents. 74% of Millennial banking customers for example, want their financial institutions to send them information about services exactly when they need to see it. This could be information about personal loans when they’re starting to look for a house, or travel insurance before they intend to travel.
Companies these days have a wealth of data on their customers. In theory, organizations should have the ability to know who they are, what they need and what makes them defect to another company. However, lack of visibility on the holistic customer journey and customer intelligence tools stunt their ability to provide such excellence.
The following section will delve into three types of analytics particularly useful for managing customer conversations — predictive, clustering and revenue-generating.
Predictive Analytics
Predictive analytics provide foresight into potential customer problems and opportunities. Extracted from existing historical conversational, transactional and behavioral data, it can help agents better prepare for customer outcomes and trends.
A pretty common example is prediction of when influxes of customer conversations come in. For eCommerce businesses, holiday seasons generally see a spike in customer conversations and steadily reduces till the next holiday season. In a more complex scenario, predictive analytics can find that customers with a particular occupation, a certain concern and at a similar stage in their lives is actually a niche the organization hasn’t capitalized on.
Cluster Analysis
Now this one isn’t as common in a conversational technology, but is definitely worth mentioning. Cluster analysis involves conversations and customer information to be tagged, then for similar or related tags to be clustered together to draw insights.
Cluster analysis can draw out how topics in conversations can be relevant, or how particular customer segments can be feel about a product. This customer intelligence can then feed into other parts of the business. It could be used to help create a new automated customer workflow for upsells, or contribute to a new marketing campaign for a newly discovered customer segment.
Revenue-generating analytics
As repetitive and menial conversations are moving towards being solved by self-service solutions, agents must also move from a traditional support role to a hybrid service-to-sales model. This category of analysis is as the name suggests, analysis that serves to generate revenue for the business within conversations.
For example, Woveon’s Intelligent Response Framework suggests ways customer specialist representatives in banks can sell more products to their customers. A customer who fits the profile of ‘customers who typically get a black American express card’ will prompt a suggestion for the agent to talk the customer into an upgrade from their current card. A customer who is at a stage in their life where ‘customers like him are looking at buying a property’ will prompt a suggestion to link some home loan webpages, or a free session with a financial planner.
In the best possible scenario, this analysis is also delivered at the right time for an agent to capitalize on the opportunity, like in an intelligent response framework.
Be a data geek, not creep
Of course, it’s important to know that use of data should be “cool”, not “creepy”. There’s a fine line between the two that should never be crossed. Also, everyone’s fine line is drawn differently, so what one customer may think is cool, can be perceived as creepy by someone else.
Enterprises should have enough data about their customers to track and understand individual preferences, and see how customers respond to different use of their information at different points in the customer journey. Conversational intelligence and analysis tools can help create better relationships without overstepping the customer’s boundaries.
On a whole, customers don’t mind companies using their data for personalizing their experience and suggesting products and services that benefit them.
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While human contact is diminishing in volume, the quality and importance of each interaction increases. Forward-thinking organizations should be balancing quantity with quality to maintain a competitive advantage in customer experience. Technology can be a great booster to that end.
Have more ways you think businesses can improve on their customer conversations? Reach out to us to add to the article. We love chatting to like-minded people!
Artificial Intelligence (AI) and machine learning (ML) is all the buzz right now, and rightfully so with the significant contributions it has made to redefining many aspects of business. However, many people are still skeptical about the application of AI and ML to enhancing customer experience.
Some would argue that machines cannot possibly take over customer service, something that has a heavy focus on human interaction. Machines lack the empathy and emotional intelligence core to providing a great customer experience. On the other hand, many also see the benefit of applying AI and ML to automate repetitive tasks, allowing humans to dedicate more time to, well, being human.
We reached out to some experts from Oovvuu, Canva and The Minerva Collective to pick their brains about the issue.
What is the current state of customer experience, and how do you see it evolve with AI & ML technology?
Present customer experience is “all over the place, with wildly varying results. Two customers using the same service can have completely different impressions of their experience, and in many cases the service is clunky and poorly structured” says Anthony Tockar, Data Scientist and Co-founder of The Minerva Collective. The unfortunate reality is that 78% of consumers have bailed on a transaction or not made an intended purchase because of poor service experience. In fact, companies only hear from 4% of its dissatisfied customers. With so much choice available to consumers, it’s much easier to find another company with similar offerings than spending time complaining or calling about a problem. Which is why there is a very real need to focus on customer experience, a factor that is becoming increasingly important to retain the modern customer.
Paul Tune, Machine Learning Engineer at Canva, believes “there are two trends in improving customer experience:
A trend towards tailoring for the individual, as more data is gathered about each customer at a large scale, and;
A trend towards providing a smooth experience for customers across multiple touchpoints by anticipating their needs. “
To demonstrate how customer experience has evolved, Paul continues with an example. “Early recommendation systems, such as the recommendation engines developed at Amazon and NetFlix in the early 2000s, provided recommendations at a much coarser level, chiefly for specific groups of customers. The granularity of recommendations in the near future is going to be much finer. For instance, an engineer from NetFlix I spoke to recently, mentioned that a subscriber’s favourite character for a TV series would appear in the menu when the TV series is selected. This means having to learn more about each customer and predicting their habits. We also see this in the form of smart personal assistants, such as Alexa and Siri” he says.
Ricky Sutton, Founder and CEO of Oovvuu, adds on that whilst AI and ML “certainly has an element to play [in customer experience], it also lacks a key element…empathy. So my thought is that it will evolve. The more AI is used, the more it learns and the better it gets, but human-level empathy remains a pipe dream for now.”
What is the biggest lesson you have learned from applying smart technology to customer experience?
For Anthony, the lesson has been the need for people using smart technology to properly understand it – “My experience is that people often don’t trust what they don’t understand. The latest technologies have been great for grabbing headlines, but only the most forward-thinking businesses are serious about applying them to derive value. This isn’t necessarily a bad thing – domain knowledge is essential for good data science, and blindly relying on new approaches has many inherent risks. There is a lot that has been learned about customer experience over time and there is a need to explain smart technology to business people using the right language to allow them to fully realise its value.”
To Paul, what matters most, is the customer’s end-to-end experience. Meaning that all the touchpoints with the customer should be seamless. For him, “the challenge with integrating smart technology to improve user experience is similar to managing any other complex system: with more moving parts, there is a higher chance of failure in the system. Naively applying machine learning to improve customer experience is misguided. Machine learning works best if it is complementary to the customer experience, serving to enhance the experience of a great product.”
“At Canva, our goal is simple: we want to give the customer the best experience in empowering them to create and design. To that end, there are two aspects that we focus on. Firstly, how do we make the content that they need for their designs easily accessible. Secondly, how do we anticipate what resources might be helpful for them in the future. We achieve these goals by improving our search and recommendation services to enhance customer experience.”
The biggest lesson for Ricky is that “AI turns humans into super-humans, but only for certain tasks.” – “When we started Oovvuu, we hired editors to read articles and find relevant videos, and they were able to read one publication each and find 40 relevant videos per day. That same person using the AI tools that we created, can now read 100,000 publishers, and 300,000 stories a day, covering 26 million topics and find relevant videos from more than 40 global broadcasters. AI is mind-blowingly powerful for automating manual human tasks, but humans remain better at all the things that, well, make us human.”
What are some challenges for businesses who try to integrate AI & ML technology and customer experience?
Anthony, Paul and Ricky all agreed that a huge challenge for businesses is not having a solid data infrastructure, or a deep understanding of what exactly should be measured to achieve business goals and customer satisfaction.
“Many companies approach us seeking to use conversational AI as a ready-made silver bullet for a business problem. Others come to ask to play with AI, so they can find a business opportunity. Neither really works.” Ricky said. “For us, the solution was to know what business problem we were trying to solve: namely, to put a relevant video into every article being published worldwide. We then used AI to solve it, but what we started with was very basic and not up to the job. We have had a team nurturing the teaching for almost 1,000 days to get it where it is.”
Anthony went on to add that “there is no silver bullet – good data scientists are required to translate these algorithms into business value. Having a solid data science strategy is essential, and through good leadership, increased data literacy and an understanding of how to build a high-performance data science team, businesses can harness these technologies to forge a competitive advantage.”
Paul concludes with another common challenge many businesses face when adopting AI & ML into their processes – the volume of data. “Present machine learning techniques rely on a relatively large amount of data to provide good predictions” he says. “While there is fundamental research being carried out presently to (hopefully) reduce the amount of data required to train these machine learning models, the current main technological limitation of requiring a huge amount of data is here to stay for the foreseeable future.” But “fortunately, this effect can be mitigated if the data collected is of sufficiently high quality.”
Are you implementing AI and ML technology in your business? Share your story with us in the comments below!
About the Contributors
Anthony Tockar
Anthony is a leader in the data science space, and has worked on problems across insurance, loyalty, technology, telecommunications, the social sector and even neuroscience. A formally-trained actuary, Anthony completed an MS in Analytics at the prestigious Northwestern University. After hitting the headlines with his posts on data privacy at Neustar, he returned to Sydney to practice as a data scientist while co-founding the Minerva Collective and the Data Science Breakfast Meetup. He also helps organise several other meetups and programs for data scientists, in line with his mission to extend the reach and impact of data to help people.
Paul Tune
Paul Tune is a Machine Learning Engineer at Canva, responsible for developing solutions for tailoring and personalising content for Canva’s customers. He has several publications in prestigious computer science conferences and journals, including the ACM SIGCOMM conference in 2015. His interests include deep learning, statistics and information theory.
Ricky Sutton
Ricky is founder and CEO of Oovvuu, an IBM and Amazon-backed start up that uses artificial intelligence to match videos from global broadcasters with publishers worldwide. It’s mission is to use AI to insert a relevant short form and long form video in every article. In doing so, it aims to tell the news in a new and more compelling way, end fake news, and in doing so, repatriate billions from Facebook and Google back to the journalists and broadcasters who make the content.
Recently I wrote an article for LinkedIn titled “Can we maintain the human touch with customer service?” I couldn’t help think about how fast we are moving with Artificial Intelligence that the question still remains, I am not worried about 5 years from now or what new customer interactions will be digital, but how will businesses maintain the reality check with their customers? Surely digital chat bots and automated ticketing systems will ask random customers surveys about what they thought about their service response and the level of happiness to refer another customer. To implement is very easy but to keep the human connection with your customers will be the challenge.
Deliver Smarter Customer Service Solutions
At Woveon, we watch and analyse through thousands of conversations all uniquely handled by diligent customer service agents who, assisted with technology, work tirelessly around the clock to acknowledge, understand, listen to and resolve the incoming customer conversation. Clearly customer service has the human touch here! Even with today’s conversational AI technology surpassing standards in reliability, accuracy and now business intelligence the human touch in AI must not be far away? This is an important consideration looking at the technology landscape today, companies are working on delivering smarter customer service solutions, from chat bots that understand your sentiment and can adapt to your tone and writing style to automated enquiry systems that can help recommend products while you shop online. Yet still, customer service and particularly conversation management is still a human “touch”, something that is defined intrinsically in the term “customer experience”.
Let’s take the example of creating an outstanding customer service experience. Data tells us that outstanding customer service increases brand loyalty. Examples include begin a conversation with a podcast, send personal messages, create a lifestyle and get back to your customers. We’re not talking about getting back to your customers via a bot or automated reply email, but rather using an actual person who understands your customers and can understand the fine details and semantics of human feelings. Remember, customer service is all about listening to your customers and putting yourself in their shoes. Great customer service professionals can quickly adapt and understand the customer’s frustrations and calm their emotions. Being present and responding quickly in human is very different to doing this via a scripted automated response. However, in the enterprise world, a study by Oracle put it at 8 out of every 10 businesses who are already implementing or about to implement AI as a customer service solution by 2020. Nearly 40% of all enterprises are already using some form of AI technology with Forrester predicting a 300% increase in AI investments, the disruptive power of AI will impact every part of the business from customer service to sales and support. So are businesses going ahead at this the wrong way?
Having interviewed several CTOs and CMOs working with the technology, there is no rushing into the game looking for the holy grail. For most, the best step moving forward is in assistive and adaptive technology or to assist with data collection and analysis. AI technology is encapsulating more and more human qualities as technology advances. Bots are often deployed to collect data based off human input and use it to optimise the customer’s experience. This is particularly applicable to personalisation. Human teams then need to help filter, sift through and make sense of all the personalisations so the system can make better judgements in the future. Artificial intelligence predicts and prioritises the user’s interests according to their searches and similar inputs given by other users. This, when compared to the pros of human service, has similar benefits to empathy and experience. For example the human touch can continue on more serious, complicated customer challenges whereas standard, mundane everyday enquiries can be handled by AI bots. An example is AI assistance to lessen waiting periods for customer inquiries. KLM, the flag carrier airline of the Netherlands, used DigitalGenius’ AI system to answer customer’s questions faster. The AI units interpreted the questions and answered them with a quick edit of the preformed answer to relate directly to the language used by the customer. It was also able to adapt to the platform for the inquiries, pumping out longer responses to emails but limiting Twitter responses to 140 characters. Digital customer service seems to be directed towards matching human interaction but with the removal of prominent flaws.
So can we maintain the human touch in customer service? Having been a product manager and worked in technology since the first dot com (no I am not that old, I was just young when I first got into technology), we can expect to see customer service significantly enhanced with AI bringing the human touch to a new level. The amount of data that AI and ML will help sift through to help “advise” and “suggest” to a customer service team will break new boundaries. Customer service teams can then be deployed to work on escalated or prioritised items that result in a big sale or help close the deal. Customer service, intuitively is tied closely with the human touch, a computer cannot learn years of successful customer interactions without first being taught and guided by humans. This is a realistic fact.
The hype around Artificial Intelligence technologies is at its peak. According to the 2017 Gartner Hype Cycle, emerging technologies such as deep learning, machine learning and virtual assistants are at the “peak of inflated expectation”. Cognitive expert advisors have passed this peak and are now descending towards the “trough of disillusionment”. This occurs when interest wanes as experiments and implementations fail to deliver.
The benefits of AI for customer experience management are potentially game changing. AI has the capability to analyse vast amounts of data in real time from various sources, including human behaviours and emotions. Expectations are high because this capability can then be used to create seamless and personalised customer experiences that are optimised to the device and channel of choice.
Pragmatists and battle hardened cynics will recall that when automation was first introduced into customer service channels, the results were often spectacularly underwhelming. So, is the application of AI to customer experiences destined to fall into the trough of disillusionment before climbing the slope of enlightenment? Or is there a path to follow to avoid the pitfalls of unmet expectations?
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Intelligently using Artificial Intelligence for Customer Experience
In order to find out whether the application of AI to your business’ customer experience will take a downturn, it is necessary to first ask yourself: What is driving your organisation’s AI strategy? Is it because:
AI is all the rage in your industry and your organisation is fearful of being left behind?
If you take the lead in implementing AI, it will make you look smarter/cooler than your colleagues?
It sounds like a cool and fun toy to experiment with?
Your organisation needs to catch up with your competitors who have been early adopters of AI?
AI is a great opportunity to reduce the cost to serve our customers?
If the answer to any of the above is Yes, then the trough of disillusionment beckons.
Alternatively, if you are deploying or considering AI because…
AI can enable your people and optimise your processes to operate more intelligently and efficiently, in order to provide individualised and predictive experiences for your customers at scale
…..then a brighter future awaits.
For these technologies to have any chance of success you should have a clear sense of purpose of how to you intend to deploy AI to drive CX in your business. Here are three ways you can use AI in a purposeful way to create meaningful customer experiences.
1. Use AI to Enhance your Knowledge of the Customer
An example would be using data analytics to anticipate the needs of individual customers at each moment of truth and key stage of their journey. Some specific examples oh how businesses are using AI to enhance customer knowledge:
2. Use AI to create stronger emotional connections with your customers
Using AI to recognise a customer’s emotional state helps agents better respond to the customer during an interaction, thereby creating stronger emotional connections.
Not only can AI empower agents with emotional intelligence to reply appropriately to customers, it can be used as a tool to connect service agents with the right information in the organisation’s knowledge base in real time. Examples of why this can be powerful to a business:
As a result, nearly two thirds of CX leaders say their organisation’s revenue growth outperforms their industry counterparts, compared with only a quarter of CX laggards. The proof is therefore clearly in the pudding: when applied in a purposeful and meaningful way, AI technology can enable organisations to increase agility and overcome competitive threats and leverage this advantage to drive acquisition.
Written by Dr Steve Nuttall – Head Of CX Research, Fifth Quadrant. Steve has worked in various leadership roles as a market research insights professional for over twenty years in Europe, Asia and Australia. He leads Fifth Quadrant’s program of CX strategy research and is an international speaker and presenter on best practice customer experience. Steve assists organisations to deliver their customer-centric strategies and business performance goals including designing and implementing programs to help optimise the customer experience.
I was in a conversation with friend this week. He’s an avid murder mystery and investigative reporting fan. He loves shows like Making of a Murderer, 60 Minutes, 20/20, and True Detective. I asked him if he was a podcast fan. He said he wasn’t. With a grin, I leaned in and said, “I’m about to change your month. Have you heard of Serial?”
We need to know our customers like we know our friends.
Serial revolutionized podcasting, coming in with over 40 million downloads in its first season as it followed a murder mystery story week by week. My buddy devoured all 12 episodes and spent countless hours reading on the web about possible theories.
And I had no doubt he would.
We need to know our customers like we know our friends. The question is, how?
Peter McCarthy, founder of The Logical Marketing Agency, has laid the groundwork for us. He describes three buckets that help you identify your customer: (1) Demographics, (2) Psychographics, and (3) Behaviors. You can read more of his work here. For the exercise, take your customer and create the below three buckets and then start asking questions. Here’s a start:
Demographics:
Where do your customers live? What’s their age? occupation? income? political affiliation? urban or rural? gender? ethnicity? These are general questions that you might find on a census. However, you must dig deep. It’s laborious, but it will serve you in the long run. At the end of this exercise you should be able to picture your customer when he/she walks through the door, and that’s huge.
Psychographics:
How do your customers think? What do they believe in? What are their attitudes towards this or that? What are their preferences? What do they love? hate? crave? What are their emotions towards a given topic? What do they value? What gets them excited?! Make a list of emotions and attach a description of your customer to each. Use what you learn to write better copy or apply it to the design of your website or, better yet, your product! When we say “we feel,” we attribute a cognitive value. Learn what your customer feels.
Behaviors:
This is where things get exciting, especially if you have access to a large data depository. What does your customer do? What do they purchase? read? use? crave? search? How do they engage with social media? Instagram more than Facebook? Twitter more than Snapchat? What are patterns that you find with your customers? Why do they drop out at point of purchase? Use A/B testing as a tool to discover behaviors.
Next time: Identifying your customer can be laborious, but it’s crucial for risk mitigation and customer growth. I’m going to introduce you to tools that will help you to identify your customer in 30 minutes. These tools are free and fun to use!
Customer service is often overlooked by many companies because it’s hard to measure the direct value of providing great customer experience.
In today’s highly competitive market, businesses have to make tough calls on what to focus on next to survive, and unfortunately, providing customer satisfaction is usually on the lower end of the list.
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Without being able to properly quantify the direct value of great service, many businesses can’t justify spending time and resources on providing it. Other businesses just see customer service more as a pain, and choose to be reactive, rather than proactive to customer expectations. However, the truth is, great customer service is absolutely crucial to the long-term survival of the business.
Customer Service Importance and its Value
You can argue that price is the main reason your customers choose you, so you focus your energy and resources on being the most affordable. I’m telling you, there will come a day when a competitor comes in with a better product for a cheaper price.
In the the same way, you can also argue that you focus on innovative developments and that’s why your customers buy from you. Again, there will come a day a competitor swoops in and take your customers from you because they made better innovative progress. Do you know what you can invest in that your competitors can’t take away from you? Loyalty. Good old customer loyalty.
The best way to gain customers’ loyalty? You charm them with your service.
By continuously engaging with them through customer service and marketing efforts, you are fostering a relationship with them. It’s hard for people to walk away from someone or something they have formed an attachment to.
So now you know why it’s important, what’s the value of providing great customer experience and service to create highly engaged customers?
The Value of Customer Service
1. Retaining customers is much less expensive than acquiring new ones
It’s true. Acquiring a new customer can be anywhere from 5-25 times more expensive than keeping a current one. You don’t have to spend so much time and resources finding a new customer and converting them. Instead, you just have to make sure they’re satisfied and will repurchase from you.
Customer support contributes a large portion to the retention and satisfaction of customers. Many companies put a lot of time and resources in their sales and marketing teams, when just as much emphasis should be put in support or success teams. When done right, they can have a bigger impact on your bottomline than new acquisition activities.
2. Repeat customers generally spend more than new ones
A study by McKinsey found that eCommerce spending for new customers on average is $24.50, compared to $54.50 for repeat customers. Even better, highly-engaged customers buy 90 percent more often and spend 60 percent more per transaction.Making a customer happy shouldn’t be a one time thing at the beginning of the relationship. Most relationships are more valuable the longer they are. Businesses should be putting more emphasis on their customer service, success and support teams because the financial growth potential is much larger than in newly acquired customers. Plus, the probability of selling to an existing customer is 60 – 70%, whereas the probability of selling to a new prospect is only 5-20%.
2. Great service reduces the severity of overall problems.
We’ve all had problems with companies before. In the moment, you might be upset, angry, annoyed or all of the above. If you had to talk to a rude customer service rep on top of all that…I’d imagine you wouldn’t be too happy. What originally may have been a small problem would go from 0 – 100.
In the same way bad service can escalate problems, good customer service can reduce them. When delivered well, customer service can diffuse negative emotions from the customer and the situation. Excellent customer service can turn the situation around into a positive. Small things like apologizing, empathizing and being genuine can go a long way to reduce a customer’s negative emotions and the severity of the overall problem.
82% of satisfied customers will “likely” or “very likely” keep shopping with a company and give it another chance if something goes wrong.
3. Builds brand awareness with minimal effort.
Customers are being more and more vocal about how a company treats them and how a company makes them feel. Considering your customer service team are likely to be the only people in contact with a customer, they play a crucial role in shaping their customer experience, and by extension, whether they have good or bad things to say about the company.
It is therefore important that every interaction with a customer should make them feel valued, listened to and supported. Extra points for going beyond expectations like Paul from Zappos. It was a simple response to a customer whose shoes were falling apart, but he made it so much more. This ended up being shared on Reddit, Hubspot and Helpscout and championed as customer service at its best.
4. People remember the service a lot longer than they remember the price.
Think back to a purchase you made a couple of years back. Heck, think back a couple of months even. I can’t even remember the price of something I bought in the last few months!
What I do remember though, is the service, the delivery and the effort I had to put in to make the purchase. It’s not about the destination, it’s about the journey. And in this case, the price you paid is the destination, and all that leading up to the purchase, is the journey. People will talk. If they liked the journey, they will recommend it to others.
5. It’s a competitive advantage no one can take away
With rapid innovation reducing differentiation between one product and another, and competitors just a click away, customer service is one of the last frontiers of sustainable competitive advantage for businesses.
Many businesses will pay lip service to the value of customer service, all the while cutting costs and resources to provide it. This will only ensure they provide the bare minimum to support customers, it doesn’t mean it’s anywhere near good enough to be a advantage. When normal customer service standard means going above and beyond for a customer, that is when customers will choose your business over someone else’s.
Customer Service Takeaway: Customer service is becoming more important than ever as competitors increase and are closer than ever. A customer is 4 times more likely to defect to a competitor if the problem is service related than price or product related. No one wants to do business with someone they don’t like. The product or service doesn’t matter anymore. They can easily substitute with other products or services that function similarly. Customers would rather deal with the slight inconvenience that the competitor’s product or service doesn’t function the same way, than to deal with the huge inconvenience of not being supported by your business.
Customer service, when delivered to the satisfaction of customers, not only creates a powerful marketing opportunity for the business, but also helps with the bottomline. Not only are return customers easier to sell to, they will spend more per transaction and would pay extra to guarantee better service.
Based in Singapore, Colin is the Director, Customer Success and Lead Data Scientist, APAC for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Over his career, Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. He frequently speaks at various global conferences. Colin is a firm believer in data-based decision making and applying AI. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
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